Apple App Store dataset to explore detailed information on app popularity, user feedback, and monetization features. Popular use cases include market trend analysis, app performance evaluation, and consumer behavior insights in the mobile app ecosystem.
Use our Apple App Store dataset to gain comprehensive insights into the mobile app ecosystem, including app popularity, user ratings, monetization features, and user feedback. This dataset covers various aspects of apps, such as descriptions, categories, and download metrics, offering a full picture of app performance and trends.
Tailored for marketers, developers, and industry analysts, this dataset allows you to track market trends, identify emerging apps, and refine promotional strategies. Whether you're optimizing app development, analyzing competitive landscapes, or forecasting market opportunities, the Apple App Store dataset is an essential tool for making data-driven decisions in the ever-evolving mobile app industry.
This dataset is versatile and can be used for various applications: - Market Analysis: Analyze app pricing strategies, monetization features, and category distribution to understand market trends and opportunities in the App Store. This can help developers and businesses make informed decisions about their app development and pricing strategies. - User Experience Research: Study the relationship between app ratings, number of reviews, and app features to understand what drives user satisfaction. The detailed review data and ratings can provide insights into user preferences and pain points. - Competitive Intelligence: Track and analyze apps within specific categories, comparing features, pricing, and user engagement metrics to identify successful patterns and market gaps. Particularly useful for developers planning new apps or improving existing ones. - Performance Prediction: Build predictive models using features like app size, category, pricing, and language support to forecast potential app success metrics. This can help in making data-driven decisions during app development. - Localization Strategy: Analyze the languages supported and regional performance to inform decisions about app localization and international market expansion.
CUSTOM Please review the respective licenses below: 1. Data Provider's License - Bright Data Master Service Agreement
Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
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App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...
In today's digital landscape, data transparency and compliance are paramount. Organizations across industries are striving to maintain trust and adhere to regulations governing data privacy and security. To support these efforts, we present our comprehensive Ads.txt and App-Ads.txt dataset.
Key Benefits of Our Dataset:
The Power of Ads.txt & App-Ads.txt: Ads.txt (Authorized Digital Sellers) and App-Ads.txt (Authorized Sellers for Apps) are industry standards developed by the Interactive Advertising Bureau (IAB) to increase transparency and combat ad fraud. These files specify which companies are authorized to sell digital advertising inventory on a publisher's website or app. Understanding and maintaining these files is essential for data compliance and the prevention of unauthorized ad sales.
How Can You Benefit? - Data Compliance: Ensure that your organization adheres to industry standards and regulations by monitoring Ads.txt and App-Ads.txt files effectively. - Ad Fraud Prevention: Identify unauthorized sellers and take action to prevent ad fraud, ultimately protecting your revenue and brand reputation. - Strategic Insights: Leverage the data in these files to gain insights into your competitors, partners, and the broader digital advertising landscape. - Enhanced Decision-Making: Make data-driven decisions with confidence, armed with accurate and up-to-date information about your advertising partners. - Global Reach: If your operations span the globe, our dataset provides insights into the Ads.txt and App-Ads.txt files of publishers worldwide.
Multiple Data Formats for Your Convenience: - CSV (Comma-Separated Values): A widely used format for easy data manipulation and analysis in spreadsheets and databases. - JSON (JavaScript Object Notation): Ideal for structured data and compatibility with web applications and APIs. - Other Formats: We understand that different organizations have different preferences and requirements. Please inquire about additional format options tailored to your needs.
Data That You Can Trust:
We take data quality seriously. Our team of experts curates and updates the dataset regularly to ensure that you receive the most accurate and reliable information available. Your confidence in the data is our top priority.
Seamless Integration:
Integrate our Ads.txt and App-Ads.txt dataset effortlessly into your existing systems and processes. Our goal is to enhance your compliance efforts without causing disruptions to your workflow.
In Conclusion:
Transparency and compliance are non-negotiable in today's data-driven world. Our Ads.txt and App-Ads.txt dataset empowers you with the knowledge and tools to navigate the complexities of the digital advertising ecosystem while ensuring data compliance and integrity. Whether you're a Data Protection Officer, a data compliance professional, or a business leader, our dataset is your trusted resource for maintaining data transparency and safeguarding your organization's reputation and revenue.
Get Started Today:
Don't miss out on the opportunity to unlock the power of data transparency and compliance. Contact us today to learn more about our Ads.txt and App-Ads.txt dataset, available in multiple formats and tailored to your specific needs. Join the ranks of organizations worldwide that trust our dataset for a compliant and transparent future.
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Nowadays, mobile applications (a.k.a., apps) are used by over two billion users for every type of need, including social and emergency connectivity. Their pervasiveness in today world has inspired the software testing research community in devising approaches to allow developers to better test their apps and improve the quality of the tests being developed. In spite of this research effort, we still notice a lack of empirical analyses aiming at assessing the actual quality of test cases manually developed by mobile developers: this perspective could provide evidence-based findings on the future research directions in the field as well as on the current status of testing in the wild. As such, we performed a large-scale empirical study targeting 1,780 open-source Android apps and aiming at assessing (1) the extent to which these apps are actually tested, (2) how well-designed are the available tests, and (3) what is their effectiveness. The key results of our study show that mobile developers still tend not to properly test their apps, possibly because of time to market requirements. Furthermore, we discovered that the test cases of the considered apps have a low (i) design quality, both in terms of test code metrics and test smells, and (ii) effectiveness when considering code coverage as well as assertion density.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Google Play Store Category wise Top 500 Apps’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shakthidhar/google-play-store-category-wise-top-500-apps on 13 February 2022.
--- Dataset description provided by original source is as follows ---
Google Play stores top 500 app data based on their rankings on January 2022 for all the available categories. Link to scraping code: https://github.com/Shakthi-Dhar/AppPin Link to backup datafiles: github data files
The dataset contains the top 500 android apps available on the google play store for the following categories: All Categories, Art & Design, Auto & Vehicles, Beauty, Books & Reference, Business, Comics, Communication, Education, Entertainment, Events, Finance, Food & Drink, Health & Fitness, House & Home, Libraries & Demo, Lifestyle, Maps & Navigation, Medical, Music & Audio, News & Magazines, Parenting, Personalization, Photography, Productivity, Shopping, Social, Sports, Tools, Travel & Local, and Video Players & Editors.
The app rankings are based on google play store app rankings for January 2022.
In Review and Downloads, the alphabet T, L, Cr represents Thousands, Lakhs, Crores as per the google play store naming convention. They are similar to M, B which represent millions, billions. 1L (1 Lakh) = 100T (100 Thousand) 10L (10 Lakhs) = 1M (1 Million) 1Cr( 1 Crore) = 10M (10 Million)
This data is not provided directly by Google, so I used Appium an automation tool with python to scrape the data from the google play store app.
Inspired by Fortune500. Fortune500 provides data on top companies in the world, so why not have a data source for top apps in the world.
--- Original source retains full ownership of the source dataset ---
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
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PLEASE UPVOTE IF YOU LIKE THIS CONTENT! 😍
Duolingo is an American educational technology company that produces learning apps and provides language certification. There main app is considered the most popular language learning app in the world.
To progress in their learning journey, each user of the application needs to complete a set of lessons in which they are presented with the words of the language they want to learn. In an infinite set of lessons, each word is applied in a different context and, on top of that, Duolingo uses a spaced repetition approach, where the user sees an already known word again to reinforce their learning.
Each line in this file refers to a Duolingo lesson that had a target word to practice.
The columns are as follows:
p_recall
- proportion of exercises from this lesson/practice where the word/lexeme was correctly recalledtimestamp
- UNIX timestamp of the current lesson/practice delta
- time (in seconds) since the last lesson/practice that included this word/lexemeuser_id
- student user ID who did the lesson/practice (anonymized)learning_language
- language being learnedui_language
- user interface language (presumably native to the student)lexeme_id
- system ID for the lexeme tag (i.e., word)lexeme_string
- lexeme tag (see below)history_seen
- total times user has seen the word/lexeme prior to this lesson/practicehistory_correct
- total times user has been correct for the word/lexeme prior to this lesson/practicesession_seen
- times the user saw the word/lexeme during this lesson/practicesession_correct
- times the user got the word/lexeme correct during this lesson/practiceThe lexeme_string
column contains a string representation of the "lexeme tag" used by Duolingo for each lesson/practice (data instance) in our experiments. The lexeme_string field uses the following format:
`surface-form/lemma
This dataset provides information on the 20 most popular digital health certificate apps in the world. It shows how many times each app has been downloaded, describes their privacy policies, and highlights any potentially invasive permissions.
The global number of KakaoTalk users in was forecast to decrease between 2024 and 2028 by in total 0.7 million users. This overall decrease does not happen continuously, notably not in 2026 and 2027. The KakaoTalk user base is estimated to amount to 48.7 million users in 2028. Notably, the number of KakaoTalk users of was continuously increasing over the past years.User figures, here concerning the platform kakaoTalk, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).
App Switch Networks DatasetGML files representing the Android smartphone application switching networks of 53 individuals.networkdata.zip
https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html
We are publishing a walking activity dataset including inertial and positioning information from 19 volunteers, including reference distance measured using a trundle wheel. The dataset includes a total of 96.7 Km walked by the volunteers, split into 203 separate tracks. The trundle wheel is of two types: it is either an analogue trundle wheel, which provides the total amount of meters walked in a single track, or it is a sensorized trundle wheel, which measures every revolution of the wheel, therefore recording a continuous incremental distance.
Each track has data from the accelerometer and gyroscope embedded in the phones, location information from the Global Navigation Satellite System (GNSS), and the step count obtained by the device. The dataset can be used to implement walking distance estimation algorithms and to explore data quality in the context of walking activity and physical capacity tests, fitness, and pedestrian navigation.
Methods
The proposed dataset is a collection of walks where participants used their own smartphones to capture inertial and positioning information. The participants involved in the data collection come from two sites. The first site is the Oxford University Hospitals NHS Foundation Trust, United Kingdom, where 10 participants (7 affected by cardiovascular diseases and 3 healthy individuals) performed unsupervised 6MWTs in an outdoor environment of their choice (ethical approval obtained by the UK National Health Service Health Research Authority protocol reference numbers: 17/WM/0355). All participants involved provided informed consent. The second site is at Malm ̈o University, in Sweden, where a group of 9 healthy researchers collected data. This dataset can be used by researchers to develop distance estimation algorithms and how data quality impacts the estimation.
All walks were performed by holding a smartphone in one hand, with an app collecting inertial data, the GNSS signal, and the step counting. On the other free hand, participants held a trundle wheel to obtain the ground truth distance. Two different trundle wheels were used: an analogue trundle wheel that allowed the registration of a total single value of walked distance, and a sensorized trundle wheel which collected timestamps and distance at every 1-meter revolution, resulting in continuous incremental distance information. The latter configuration is innovative and allows the use of temporal windows of the IMU data as input to machine learning algorithms to estimate walked distance. In the case of data collected by researchers, if the walks were done simultaneously and at a close distance from each other, only one person used the trundle wheel, and the reference distance was associated with all walks that were collected at the same time.The walked paths are of variable length, duration, and shape. Participants were instructed to walk paths of increasing curvature, from straight to rounded. Irregular paths are particularly useful in determining limitations in the accuracy of walked distance algorithms. Two smartphone applications were developed for collecting the information of interest from the participants' devices, both available for Android and iOS operating systems. The first is a web-application that retrieves inertial data (acceleration, rotation rate, orientation) while connecting to the sensorized trundle wheel to record incremental reference distance [1]. The second app is the Timed Walk app [2], which guides the user in performing a walking test by signalling when to start and when to stop the walk while collecting both inertial and positioning data. All participants in the UK used the Timed Walk app.
The data collected during the walk is from the Inertial Measurement Unit (IMU) of the phone and, when available, the Global Navigation Satellite System (GNSS). In addition, the step count information is retrieved by the sensors embedded in each participant’s smartphone. With the dataset, we provide a descriptive table with the characteristics of each recording, including brand and model of the smartphone, duration, reference total distance, types of signals included and additionally scoring some relevant parameters related to the quality of the various signals. The path curvature is one of the most relevant parameters. Previous literature from our team, in fact, confirmed the negative impact of curved-shaped paths with the use of multiple distance estimation algorithms [3]. We visually inspected the walked paths and clustered them in three groups, a) straight path, i.e. no turns wider than 90 degrees, b) gently curved path, i.e. between one and five turns wider than 90 degrees, and c) curved path, i.e. more than five turns wider than 90 degrees. Other features relevant to the quality of collected signals are the total amount of time above a threshold (0.05s and 6s) where, respectively, inertial and GNSS data were missing due to technical issues or due to the app going in the background thus losing access to the sensors, sampling frequency of different data streams, average walking speed and the smartphone position. The start of each walk is set as 0 ms, thus not reporting time-related information. Walks locations collected in the UK are anonymized using the following approach: the first position is fixed to a central location of the city of Oxford (latitude: 51.7520, longitude: -1.2577) and all other positions are reassigned by applying a translation along the longitudinal and latitudinal axes which maintains the original distance and angle between samples. This way, the exact geographical location is lost, but the path shape and distances between samples are maintained. The difference between consecutive points “as the crow flies” and path curvature was numerically and visually inspected to obtain the same results as the original walks. Computations were made possible by using the Haversine Python library.
Multiple datasets are available regarding walking activity recognition among other daily living tasks. However, few studies are published with datasets that focus on the distance for both indoor and outdoor environments and that provide relevant ground truth information for it. Yan et al. [4] introduced an inertial walking dataset within indoor scenarios using a smartphone placed in 4 positions (on the leg, in a bag, in the hand, and on the body) by six healthy participants. The reference measurement used in this study is a Visual Odometry System embedded in a smartphone that has to be worn at the chest level, using a strap to hold it. While interesting and detailed, this dataset lacks GNSS data, which is likely to be used in outdoor scenarios, and the reference used for localization also suffers from accuracy issues, especially outdoors. Vezovcnik et al. [5] analysed estimation models for step length and provided an open-source dataset for a total of 22 km of only inertial walking data from 15 healthy adults. While relevant, their dataset focuses on steps rather than total distance and was acquired on a treadmill, which limits the validity in real-world scenarios. Kang et al. [6] proposed a way to estimate travelled distance by using an Android app that uses outdoor walking patterns to match them in indoor contexts for each participant. They collect data outdoors by including both inertial and positioning information and they use average values of speed obtained by the GPS data as reference labels. Afterwards, they use deep learning models to estimate walked distance obtaining high performances. Their results share that 3% to 11% of the data for each participant was discarded due to low quality. Unfortunately, the name of the used app is not reported and the paper does not mention if the dataset can be made available.
This dataset is heterogeneous under multiple aspects. It includes a majority of healthy participants, therefore, it is not possible to generalize the outcomes from this dataset to all walking styles or physical conditions. The dataset is heterogeneous also from a technical perspective, given the difference in devices, acquired data, and used smartphone apps (i.e. some tests lack IMU or GNSS, sampling frequency in iPhone was particularly low). We suggest selecting the appropriate track based on desired characteristics to obtain reliable and consistent outcomes.
This dataset allows researchers to develop algorithms to compute walked distance and to explore data quality and reliability in the context of the walking activity. This dataset was initiated to investigate the digitalization of the 6MWT, however, the collected information can also be useful for other physical capacity tests that involve walking (distance- or duration-based), or for other purposes such as fitness, and pedestrian navigation.
The article related to this dataset will be published in the proceedings of the IEEE MetroXRAINE 2024 conference, held in St. Albans, UK, 21-23 October.
This research is partially funded by the Swedish Knowledge Foundation and the Internet of Things and People research center through the Synergy project Intelligent and Trustworthy IoT Systems.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Global Lagrangian dataset of Marine litter
This dataset regroups 12 yearly files (global-marine-litter-[2010–2021].nc) combining monthly releases of 32,300 particles initially distributed across the globe following global Mismanaged Plastic Waste (MPW) inputs. The particles are advected with OceanParcels (Delandmeter, P and E van Sebille, 2019) using ocean surface velocity, a wind drag coefficient of 1%, and a small random walk component with a uniform horizontal turbulent diffusion coefficient of Kh = 1m2s-1 representing unresolved turbulent motions in the ocean (see Chassignet et al. 2021 for more details).
Global oceanic current and atmospheric wind
Ocean surface velocities are obtained from GOFS3.1, a global ocean reanalysis based on the HYbrid Coordinate Ocean Model (HYCOM) and the Navy Coupled Ocean Data Assimilation (NCODA; Chassignet et al., 2009; Metzger et al., 2014). NCODA uses a three-dimensional (3D) variational scheme and assimilates satellite and altimeter observations as well as in-situ temperature and salinity measurements from moored buoys, Expendable Bathythermographs (XBTs), Argo floats (Cummings and Smedstad, 2013). Surface information is projected downward into the water column using Improved Synthetic Ocean Profiles (Helber et al., 2013). The horizontal resolution and the temporal frequency for the GOF3.1 outputs are 1/12° (8 km at the equator, 6 km at mid-latitudes) and 3-hourly, respectively. Details on the validation of the ocean circulation model are available in Metzger et al. (2017).
Wind velocities are obtained from JRA55, the Japanese 55-year atmospheric reanalysis. The JRA55, which spans from 1958 to the present, is the longest third-generation reanalysis that uses the full observing system and a 4D advanced data assimilation variational scheme. The horizontal resolution of JRA55 is about 55 km and the temporal frequency is 3-hourly (see Tsujino et al. (2018) for more details).
Marine Litter Sources
The marine litter sources are obtained by combining MPW direct inputs from coastal regions, which are defined as areas within 50 km of the coastline (Lebreton and Andrady 2019), and indirect inputs from inland regions via rivers (Lebreton et al. 2017).
File Format
The locations (lon, lat), the corresponding weight (tons), and the source (1: land, 0: river) associated with the 32,300 particles are described in the file initial-location-global.csv. The particle trajectories are regrouped into yearly files (marine-litter-[2010–2021].nc) which contain 12 monthly releases, resulting in a total of 387,600 trajectories per file. More precisely, in each of the yearly files, the first 32,300 lines contain the trajectories of particles released on January 1st, then lines 32,301–64,600 contain the trajectories of particles released on February 1st, and so on. The trajectories are recorded daily and are advected from their release until 2021-12-31, resulting in longer time series for earlier years of the dataset.
References
Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M., Cummings, J., Halliwell, G. R., et al. (2009). U.S. GODAE: global ocean prediction with the hybrid coordinate ocean model (HYCOM). Oceanography 22, 64–75. doi: 10.5670/oceanog.2009.39
Chassignet, E. P., Xu, X., and Zavala-Romero, O. (2021). Tracking Marine Litter With a Global Ocean Model: Where Does It Go? Where Does It Come From?. Frontiers in Marine Science, 8, 414, doi: 10.3389/fmars.2021.667591
Cummings, J. A., and Smedstad, O. M. (2013). “Chapter 13: variational data assimilation for the global ocean”, in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Vol. II, eds S. Park and L. Xu (Berlin: Springer), 303–343. doi: 10.1007/978-3-642-35088-7_13
Delandmeter, P., and van Sebille, E. (2019). The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Geosci. Model Dev. 12, 3571–3584. doi: 10.5194/gmd-12-3571-2019
Helber, R. W., Townsend, T. L., Barron, C. N., Dastugue, J. M., and Carnes, M. R. (2013). Validation Test Report for the Improved Synthetic Ocean Profile (ISOP) System, Part I: Synthetic Profile Methods and Algorithm. NRL Memo. Report, NRL/MR/7320—13-9364 Hancock, MS: Stennis Space Center.
Metzger, E. J., Smedstad, O. M., Thoppil, P. G., Hurlburt, H. E., Cummings, J. A., Wallcraft, A. J., et al. (2014). US Navy operational global ocean and Arctic ice prediction systems. Oceanography 27, 32–43, doi: 10.5670/oceanog.2014.66.
Metzger, E., Helber, R. W., Hogan, P. J., Posey, P. G., Thoppil, P. G., Townsend, T. L., et al. (2017). Global Ocean Forecast System 3.1 validation test. Technical Report. NRL/MR/7320–17-9722. Hancock, MS: Stennis Space Center, 61.
Lebreton, L., and Andrady, A. (2019). Future scenarios of global plastic waste generation and disposal. Palgrave Commun. 5:6, doi: 10.1057/s41599-018-0212-7.
Lebreton, L., van der Zwet, J., Damsteeg, J. W., Slat, B., Andrady, A., and Reisser, J. (2017). River plastic emissions to the world’s oceans. Nat. Commun. 8:15611, doi: 10.1038/ncomms15611.
Tsujino H., S. Urakawa, H. Nakano, R.J. Small, W.M. Kim, S.G. Yeager, G. Danabasoglu, T. Suzuki, J.L. Bamber, M. Bentsen, C. Böning, A. Bozec, E.P. Chassignet, E. Curchitser, F. Boeira Dias, P.J. Durack, S.M. Griffies, Y. Harada, M. Ilicak, S.A. Josey, C. Kobayashi, S. Kobayashi, Y. Komuro, W.G. Large, J. Le Sommer, S.J. Marsland, S. Masina, M. Scheinert, H. Tomita, M. Valdivieso, and D. Yamazaki, 2018. JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do). Ocean Modelling, 130, 79-139, doi: 10.1016/j.ocemod.2018.07.002.
WorldPop produces different types of gridded population count datasets, depending on the methods used and end application.
Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.
Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below.
These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country.
They can also be visualised and explored through the woprVision App.
The remaining datasets in the links below are produced using the "top-down" method,
with either the unconstrained or constrained top-down disaggregation method used.
Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs.
Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively):
- Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019)
-Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019).
-Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets.
-Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using
constrained top-down methods for all countries of the World for 2020.
-Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using
constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national
population estimates (UN 2019).
Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent.
Data for earlier dates is available directly from WorldPop.
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
DroidLeaks features 292 diverse resource leak bugs in popular and large-scale open-source Android apps. For each bug, DroidLeaks provides links to:
1. the code repository of the app subject
2. the concerned resource class
3. the buggy code revision (and buggy file and method names)
4. the bug-fixing code revision (i.e., link to the patch)
5. the bug report or the corresponding pull request for patches (if located)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘U.S. News and World Report’s College Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/flyingwombat/us-news-and-world-reports-college-data on 28 January 2022.
--- Dataset description provided by original source is as follows ---
Statistics for a large number of US Colleges from the 1995 issue of US News and World Report.
A data frame with 777 observations on the following 18 variables.
Private A factor with levels No and Yes indicating private or public university
Apps Number of applications received
Accept Number of applications accepted
Enroll Number of new students enrolled
Top10perc Pct. new students from top 10% of H.S. class
Top25perc Pct. new students from top 25% of H.S. class
F.Undergrad Number of fulltime undergraduates
P.Undergrad Number of parttime undergraduates
Outstate Out-of-state tuition
Room.Board Room and board costs
Books Estimated book costs
Personal Estimated personal spending
PhD Pct. of faculty with Ph.D.’s
Terminal Pct. of faculty with terminal degree
S.F.Ratio Student/faculty ratio
perc.alumni Pct. alumni who donate
Expend Instructional expenditure per student
Grad.Rate Graduation rate
This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.
The dataset was used in the ASA Statistical Graphics Section’s 1995 Data Analysis Exposition.
--- Original source retains full ownership of the source dataset ---
http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/
https://static.openfoodfacts.org/images/misc/openfoodfacts-logo-en-178x150.png" alt="Open Food Facts">
Open Food Facts is a free, open, collbarative database of food products from around the world, with ingredients, allergens, nutrition facts and all the tidbits of information we can find on product labels.
Open Food Facts is a non-profit association of volunteers. 5000+ contributors like you have added 100 000+ products from 150 countries using our Android, iPhone or Windows Phone app or their camera to scan barcodes and upload pictures of products and their labels.
Data about food is of public interest and has to be open. The complete database is published as open data and can be reused by anyone and for any use. Check-out the cool reuses or make your own!
The dataset contains a single table, FoodFacts
, in CSV form in FoodFacts.csv
and in SQLite form in database.sqlite
.
The columns in FoodFacts
are as follows:
WorldPop produces different types of gridded population count datasets, depending on the methods used and end application.
Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.
Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below.
These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country.
They can also be visualised and explored through the woprVision App.
The remaining datasets in the links below are produced using the "top-down" method,
with either the unconstrained or constrained top-down disaggregation method used.
Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs.
Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively):
- Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019)
-Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using
unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019).
-Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets.
-Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using
constrained top-down methods for all countries of the World for 2020.
-Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using
constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national
population estimates (UN 2019).
Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent.
Data for earlier dates is available directly from WorldPop.
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645
Occurrences of Animalia Chordata Aves recorded by users of the Birda mobile app (https://birda.org).
Species data use the IOC taxonomy (https://www.worldbirdnames.org/new/).
Data imported into Birda from external sources (e.g. other birding apps) are excluded from this dataset to avoid the potential duplication of records that may have been previously published to the GBIF by another organisation.
Occurrences deemed unreliable or suspicious are excluded from the dataset (see the section on quality control for further details).
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This dataset provides details on the 10,000 most popular films globally, sourced from The Movie Database (TMDb) via its read API. TMDb is a crowd-sourced movie information database widely used by various film-related platforms and applications. The dataset is ideal for film-related analysis, building recommender systems, and natural language processing tasks, even for those new to data analysis, as it contains some missing values.
The dataset is provided in a CSV file format. It comprises approximately 10,000 individual movie records. While exact row and record counts are not specified, the dataset is structured as tabular data, with each row representing a unique movie entry and columns detailing various attributes.
This dataset is well-suited for a variety of applications, including: * Developing and enhancing film-related consoles, websites, and mobile applications. * Creating movie recommender systems. * Performing data visualisations related to film trends and popularity. * Conducting natural language processing (NLP) tasks on movie overviews. * Data analysis and exploration, particularly for those looking to practise handling missing data.
The dataset covers movies from across the world, offering a global scope. While a specific time range for the movies is not explicitly stated, the data is fetched from TMDb, which updates its API periodically. It's noted that the dataset includes some null values where information was missing from the original TMDb database.
CCO
This dataset is intended for a broad audience including: * Young analysts: To practise data cleaning and analysis with datasets containing missing values. * Developers: For integrating movie information into media managers, mobile apps, and social sites. * Researchers: For studies on movie popularity, audience reception, and content analysis. * Data scientists: For building and testing machine learning models such as recommender systems and NLP models.
Original Data Source: Popular Movies of IMDb
Published by Collins Bartholomew in partnership with Global System for Mobile Communications (GSMA), the Mobile Coverage Explorer is a raster data representation of the area covered by mobile cellular networks around the world. The dataset series is supplied as raster Data_MCE (operators) and Data_OCI (OpenCellID database). Data_MCE global coverage has been sourced from the network operators and created from submissions made directly to Collins Bartholomew or to GSMA. The dataset series is provided at Global and National level. Global datasets contain the merged global coverages with the following file naming convention. MCE_Global
The World Database on Protected Areas (WDPA) is the largest assembly of data on the world's terrestrial and marine protected areas, containing more than 260,000 protected areas as of August 2020, with records covering 245 countries and territories throughout the world.
The WDPA is a joint venture between the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) and the International Union for Conservation of Nature (IUCN) World Commission on Protected Areas (WCPA).
Data for the WDPA is collected from international convention secretariats, governments and collaborating NGOs, but the role of custodian is allocated to the Protected Areas Programme of UNEP-WCMC, based in Cambridge, UK, who have hosted the database since its creation in 1981. The WDPA is updated on a monthly basis, and can be downloaded from https://www.protectedplanet.net/en/thematic-areas/wdpa.
Data creation: 2020-08-01
Citation:
IUCN and UNEP-WCMC (2020), The World Database on Protected Areas (WDPA) [https://www.protectedplanet.net/en/search-areas?filters%5Bdb_type%5D%5B%5D=wdpa&geo_type=region], [08/2020], Cambridge, UK: UNEP-WCMC. Available at: www.protectedplanet.net.
Contact points:
Metadata Contact: UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)
Responsible Party: UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)
Resource Contact: Protected Planet (WDPA) UNEP-WCMC & IUCN
Resource constraints:
PLEASE READ THESE TERMS AND CONDITIONS CAREFULLY. IF YOU DO NOT AGREE TO ANY OF THE TERMS AND CONDITIONS DO NOT DOWNLOAD. BY DOWNLOADING THE WDPA MATERIALS MADE AVAILABLE ON PROTECTEDPLANET.NET YOU ACCEPT AND AGREE TO COMPLY WITH THE TERMS AND CONDITIONS BELOW.
No Commercial Use
Neither (a) the WDPA Materials nor (b) any work derived from or based upon the WDPA Materials (“Derivative Works") may be put to Commercial Use without the prior written permission of UNEP-WCMC. For the purposes of these Terms and Conditions, “Commercial Use" means a) any use for profit or to generate revenue, or b) any use by an individual or entity operating within or on behalf of or to the benefit of or to assist the activities of any entity other than a not-for-profit organisation. To apply for permission for Commercial Use of the WDPA Materials please send an email to business-support@unep-wcmc.org outlining your needs.
No Sub-licensing or Redistribution of WDPA Data
The WDPA Materials may not be sub-licensed in whole or in part including within Derivative Works without the prior written permission of UNEP-WCMC. You may not redistribute the WDPA Data contained in the WDPA in whole or in part by any means including (but not limited to) electronic formats such as web downloads, through web services, through interactive web maps (including mobile applications) that grant users download access, KML Files or through file transfer protocols. If you know of others who wish to use the WDPA Data please refer them to protectedplanet.net. If you wish to provide a service through which the WDPA Data are downloadable or otherwise made available for redistribution you must contact protectedareas@unep-wcmc.org for permission and further guidance. More information on the WDPA license at https://www.unep-wcmc.org/policies/wdpa-data-licence#data_policy
Map Disclaimer:
The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Final status of the Abyei area is not yet determined.
Online resources:
Dimension Translator Json file
Dimensions labels jsonstat file
Download World Database on Protected Areas (WDPA) from Protected Planet
Apple App Store dataset to explore detailed information on app popularity, user feedback, and monetization features. Popular use cases include market trend analysis, app performance evaluation, and consumer behavior insights in the mobile app ecosystem.
Use our Apple App Store dataset to gain comprehensive insights into the mobile app ecosystem, including app popularity, user ratings, monetization features, and user feedback. This dataset covers various aspects of apps, such as descriptions, categories, and download metrics, offering a full picture of app performance and trends.
Tailored for marketers, developers, and industry analysts, this dataset allows you to track market trends, identify emerging apps, and refine promotional strategies. Whether you're optimizing app development, analyzing competitive landscapes, or forecasting market opportunities, the Apple App Store dataset is an essential tool for making data-driven decisions in the ever-evolving mobile app industry.
This dataset is versatile and can be used for various applications: - Market Analysis: Analyze app pricing strategies, monetization features, and category distribution to understand market trends and opportunities in the App Store. This can help developers and businesses make informed decisions about their app development and pricing strategies. - User Experience Research: Study the relationship between app ratings, number of reviews, and app features to understand what drives user satisfaction. The detailed review data and ratings can provide insights into user preferences and pain points. - Competitive Intelligence: Track and analyze apps within specific categories, comparing features, pricing, and user engagement metrics to identify successful patterns and market gaps. Particularly useful for developers planning new apps or improving existing ones. - Performance Prediction: Build predictive models using features like app size, category, pricing, and language support to forecast potential app success metrics. This can help in making data-driven decisions during app development. - Localization Strategy: Analyze the languages supported and regional performance to inform decisions about app localization and international market expansion.
CUSTOM Please review the respective licenses below: 1. Data Provider's License - Bright Data Master Service Agreement